Energy and cost integration model for multi-objective optimisation in turning process of stainless steel 316
The machinability of stainless steel has attracted considerable interest because of its medium strength, low carbon and corrosion resistance. Cutting fluids that are oil-based are unsustainable as the machining process has an environmental impact. Dry machining is a sustainable solution that reduces...
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| Format: | Thesis |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/30007/1/Energy%20and%20cost%20integration%20model%20for%20multi-objective%20optimisation%20in%20turning%20process.pdf |
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| Summary: | The machinability of stainless steel has attracted considerable interest because of its medium strength, low carbon and corrosion resistance. Cutting fluids that are oil-based are unsustainable as the machining process has an environmental impact. Dry machining is a sustainable solution that reduces both energy consumption and machining cost. This study aims to optimize an integrated mathematical model for both energy and cost in the turning process of stainless steel 316 (SS316). It is set out to optimize power consumption, machining cost and the traditional machining responses of surface roughness and tool wear by adjusting machining parameters. Stainless steel 316 was turned with different cutting tool types of uncoated carbide and coated tools. Three factors are associated with cutting parameters, namely cutting speed, feed rate, and depth of cut. Analysis of variance and the regression model was used to analyze the machining parameters and responses. A multi-objective optimization method was employed to optimize machining parameters in terms of energy and cost models. With a simulation in Design Expert and Matlab, the multi-response optimization problems were solved with a response surface methodology (RSM) and non-dominated sorting genetic algorithm (NSGA II), as well as integration between them. Results indicated that the minimum power consumption was obtained at the lowest cutting speed value and at the greatest values of feed rate and depth of cut, which contributed 37.43% and 20.5%, respectively. Surface roughness was minimized when feed rate and depth of cut were at their lowest levels, whereas the cutting speed was the most significant factor on tool wear, with a contribution of 39%, followed by depth of cut at 14.3%, although there was no influence by feed rate. Results also showed an improvement in power consumption under dry conditions, at 6.78%, whereas machining cost was better by 11.89% and there was acceptable quality compared to flood conditions. For the RSM method, the desirability value (0.885) and the minimum value of responses can be achieved at a cutting speed of 110 m/min, feed rate of 0.192 mm/rev, and 0.8 mm for depth of cut. This parameter combination results in an energy saving of 9.2% and reduced machining cost of 4.6%. For the integrated (RSM-NSGA II) method, the optimum objective values are 0.57-3.84 kWh and RM 8.94-9.78 for dry and flood, respectively. The results showed an improvement in energy saving of 14.94%, surface roughness of 4.71%, tool wear of 13.98%, and decreased machining cost of 4.6%. A three-confirmation method was used to validate the optimum point. Moreover, the second-generation results of optimization using NSGA II showed an improvement of more than 70% compared with that of RSM optimization. Therefore, this method also effectively reduces the effects and costs of the machining process and preserves the environment, which results in an overall enhancement of sustainable machining. |
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